Determinants Of Tissue Iron Loading In Transfused and Non-Transfused β-Thalassemia and Sickle Cell Disease Patients – Biomarkers Of Iron Regulation and Inflammation
Bibliographic record
Abstract
Introduction Transfused thalassemia (Thal) and sickle cell disease (SCD) patients develop tissue iron loading and organ injury. Even non-transfused Thal patients develop iron overload and iron-induced organ injury. In general, Thal patients are known to have greater extra-hepatic iron loading than SCD patients. This study investigated biomarkers that could modulate tissue iron loading in Thal and SCD. Patients and Methods Seventy-eight Thal and SCD patients were enrolled including transfused (17 Tx Thal, and 19 Tx SCD) and non-transfused (12 nTx Thal and 16 nTx SCD). The following were analyzed: markers of erythroid expansion GDF15 and sTfR; markers of iron regulation and loading including hepcidin, NTBI, transferrin saturation, serum ferritin, ferritin/hepcidin and liver iron concentration (LIC); and a marker of inflammation, high sensitivity C-reactive protein (CRP). LIC was measured by SQUID Ferritometry®. Serum hepcidin was determined by competitive ELISA (Intrinsic LifeSciences, La Jolla, CA). Results LIC is similar between Tx Thal, nTx Thal and Tx SCD patients. Only nTx SCD patients had lower levels of LIC. However, all thal patients had an 83% increase in GDF15 levels and 87% higher NTBI levels (p<0.001) compared to all SCD patients. nTx thal had lower NTBI compared to TX thal despite similar LIC and not being on chelation therapy. Also sTfR was 80% higher in nTx thal and SCD compared to Tx Thal. Inflammation, on the other hand, as measured by CRP was 62% higher in all SCD patients (p=0.038) relative to all thal. Also Tx SCD patients had a strong negative relationship between NTBI and sTfR (r=-0.598, p=0.018) indicating that both inflammation and erythropoietic drive may be important in extra-hepatic tissue iron loading. Tx Thal and SCD patients had at least 70% higher levels of hepcidin. nTx thal had 93% higher levels of the ratio of ferritin to hepcidin (p=0.023). NTBI and LIC were found to positively correlate in nTx Thal patients (r=0.788, p=0.012), whereas no correlation between NTBI and LIC was found in Tx Thal patients. In nTx Thal, GDF15 is also negatively correlated with hepcidin (r=-0.631, p=0.038). In Tx SCD, GDF15 positively correlates with hsCRP (r=0.784, p=0.001). Tx SCD patients also show positive correlations between GDF15 and LIC (r=0.692, p=0.003) and serum ferritin (r=0.545, p=0.024). Finally, there is a negative relationship between GDF15 and hemoglobin in both Tx Thal and nTx Thal (r=-0.574, p=0.016 ; r=-0.643, p=0.033, respectively). Conclusions The data presented here suggests that more effective erythropoiesis in nTx SCD, Tx SCD and nTx Thal seems to protect from excess NTBI and possible extra-hepatic iron loading. In Tx SCD the positive correlation between CRP and GDF15 and the negative correlation between NTBI and sTfR suggests both inflammation and effective erythropoiesis may be important in extra-hepatic iron loading. Furthermore, GDF15 could be related to ineffective erythropoiesis while sTfR is reflecting effective erythropoiesis in this study cohort. Disclosures: Walter: Novartis: Research Funding. Nemeth: Intrinsic LifeSciences: Equity Ownership, Membership on an entity’s Board of Directors or advisory committees. Ganz: Intrinsic LifeSciences: Equity Ownership, Membership on an entity’s Board of Directors or advisory committees. Porter: Shire: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy. Harmatz: Novartis: Research Funding; Shire: Consultancy, Honoraria, Research Funding, travel, travel Other.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".